用于户外生活垃圾收集的视触觉操作
Visual-tactile manipulation to collect household waste in outdoor
AI总结：
本研究提出一种集成光学触觉传感器、RGBD相机与LiDAR的户外机器人垃圾收集感知系统，通过视觉与触觉模块实现垃圾定位、识别及稳定抓取，性能优异且运行高效。
摘要：
本研究提出了一种应用于机器人操作的感知系统，可在户外环境中辅助导航、生活垃圾分类与收集工作。该系统由光学触觉传感器、RGBD相机和LiDAR组成，这些传感器集成在搭载机械臂与机器人夹爪的移动平台上。系统分为三个软件模块，其中两个基于视觉，最后一个基于触觉。基于视觉的模块使用CNN（卷积神经网络）对固体生活垃圾进行定位与识别，并估计抓取点。基于触觉的模块同样使用CNN和图像处理技术，根据触觉数据调整夹爪开度以控制抓取过程。该方案的定位误差约为6%，识别准确率达98%，在91%的抓取尝试中可保证抓取稳定性，三个模块的总运行时间低于750毫秒。
英文摘要：
This work presents a perception system applied to robotic manipulation, that is able to assist in navigation, household waste classification and collection in outdoor environments. This system is made up of optical tactile sensors, RGBD cameras and a LiDAR. These sensors are integrated on a mobile platform with a robot manipulator and a robotic gripper. Our system is divided in three software modules, two of them are vision-based and the last one is tactile-based. The vision-based modules use CNNs to localize and recognize solid household waste, together with the grasping points estimation. The tactile-based module, which also uses CNNs and image processing, adjusts the gripper opening to control the grasping from touch data. Our proposal achieves localization errors around 6 %, a recognition accuracy of 98% and ensures the grasping stability the 91% of the attempts. The sum of runtimes of the three modules is less than 750 ms.
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